diff --git a/mindspore/nn/layer/quant.py b/mindspore/nn/layer/quant.py
index 0bb69b4476..79661f72e1 100644
--- a/mindspore/nn/layer/quant.py
+++ b/mindspore/nn/layer/quant.py
@@ -419,8 +419,11 @@ class Conv2dBnFoldQuant(Cell):
         padding (int): Implicit paddings on both sides of the input. Default: 0.
         eps (float): Parameters for BatchNormal. Default: 1e-5.
         momentum (float): Parameters for BatchNormal op. Default: 0.997.
+        has_bias (bool): Specifies whether the layer uses a bias vector. Default: False.
         weight_init (Union[Tensor, str, Initializer, numbers.Number]): Initializer for the
             convolution kernel. Default: 'normal'.
+        bias_init (Union[Tensor, str, Initializer, numbers.Number]): Initializer for the
+            bias vector. Default: 'zeros'.
         beta_init (Union[Tensor, str, Initializer, numbers.Number]): Initializer for the
             beta vector. Default: 'zeros'.
         gamma_init (Union[Tensor, str, Initializer, numbers.Number]): Initializer for the
@@ -460,7 +463,9 @@ class Conv2dBnFoldQuant(Cell):
                  group=1,
                  eps=1e-5,
                  momentum=0.997,
+                 has_bias=False,
                  weight_init='normal',
+                 bias_init='zeros',
                  beta_init='zeros',
                  gamma_init='ones',
                  mean_init='zeros',
@@ -484,6 +489,7 @@ class Conv2dBnFoldQuant(Cell):
         self.group = group
         self.eps = eps
         self.momentum = momentum
+        self.has_bias = has_bias
         self.quant_delay = quant_delay
         self.freeze_bn = freeze_bn
         self.fake = fake
@@ -516,6 +522,11 @@ class Conv2dBnFoldQuant(Cell):
             weight_shape = [out_channels, in_channels // group, *self.kernel_size]
             channel_axis = 0
         self.weight = Parameter(initializer(weight_init, weight_shape), name='weight')
+        self.bias_add = P.BiasAdd()
+        if check_bool(has_bias):
+            self.bias = Parameter(initializer(bias_init, [out_channels]), name='bias')
+        else:
+            self.bias = None
 
         # initialize BatchNorm Parameter
         self.gamma = Parameter(initializer(gamma_init, [out_channels]), name='gamma')
@@ -562,6 +573,8 @@ class Conv2dBnFoldQuant(Cell):
 
     def construct(self, x):
         out_conv = self.conv(x, self.weight)
+        if self.has_bias:
+            out_conv = self.bias_add(out_conv, self.bias)
         # BN fold1
         batch_mean, batch_std, running_mean, running_std = self.batchnorm_fold(out_conv,
                                                                                self.moving_mean,
@@ -572,6 +585,8 @@ class Conv2dBnFoldQuant(Cell):
         if self.fake:
             weight = self.fake_quant_weight(weight)
         out = self.conv(x, weight)
+        if self.has_bias:
+            out = self.bias_add(out, self.bias)
         # BN fold2
         if self.is_gpu:
             if self.training: